The Product Manager, Enterprise Data & AI will be part of our Enterprise Data + AI Product organization and will help turn business problems into products, solutions and experiences that create measurable value across NBCUniversal. This is a hands-on product builder role at the intersection of business, product, data and AI. The Product Manager will work directly with business and product teams to understand how they work, uncover meaningful problems and opportunities, connect those needs to our enterprise capabilities, and help move ideas from discovery through validation and adoption. The ideal candidate combines strong product judgment with business curiosity, technical fluency and a bias toward action. You should be equally comfortable talking with a business stakeholder, understanding how a data or AI capability works, shaping a use case, building or prototyping a solution with modern tools, developing a clear product story and determining whether the resulting experience is creating value. This is not a traditional Product Manager role centered solely on managing a feature backlog. It is a role for someone who thrives in ambiguity, operates with high ownership and uses technology… particularly AI, to shorten the distance between an idea and a meaningful business outcome. What you will own (day-to-day): Business discovery and stakeholder engagement to identify meaningful problems, opportunities and use cases Translation of business needs into clear product opportunities, hypotheses, outcomes and success measures Rapid prototyping and experimentation to make ideas tangible and accelerate learning Product positioning, demos, enablement and other go-to-market motions that drive understanding and adoption Voice of the Customer and product feedback loops that inform future priorities Adoption and business-value measurement for priority initiatives Clear communication between business, Product, Data, Engineering and AI teams AI-enabled ways of working that increase speed, quality and individual leverage Responsibilities: Business Discovery & Use-Case Development: Build trusted relationships with stakeholders across the enterprise, understand their workflows and proactively uncover problems, opportunities and unmet needs — including needs that may not initially be well articulated. Product Opportunity Development: Translate stakeholder needs into clear use cases, product opportunities, hypotheses and measurable outcomes; connect those opportunities to existing enterprise products and capabilities whenever possible. Build, Prototype & Validate: Use modern AI-assisted development, prototyping and workflow tools to rapidly bring ideas to life, test assumptions and create tangible experiences that accelerate learning and decision-making. Product Adoption & Enablement: Develop the positioning, demonstrations, enablement materials and engagement motions necessary to help stakeholders understand, adopt and successfully use enterprise products and capabilities. Voice of the Customer & Feedback Loops: Establish structured feedback loops using stakeholder input, qualitative insights, usage signals and business outcomes to identify friction, unmet needs and opportunities for improvement. Data & Success Measurement: Define and track appropriate measures of adoption, engagement and business value; use quantitative and qualitative data to tell a clear story and drive decisions. Product Strategy Partnership: Bring business, customer and adoption signal back to Product Managers and product leaders to help inform roadmap priorities, investment decisions and future portfolio direction. Cross-Functional Leadership: Partner across Product Management, Product Operations, Engineering, Data, AI and business teams to determine the right path from opportunity to scalable solution without relying on formal authority. Translate Complexity into Clarity: Make sophisticated data, technology and AI capabilities understandable and actionable through clear product narratives, demos, executive-ready materials and reusable enablement assets. AI-Enabled Ways of Working: Apply AI throughout discovery, research, analysis, prototyping, documentation and workflow automation, while continually identifying new opportunities to increase organizational leverage and self-service. Primary Stakeholders: Product Managers and Product Operations teams across Enterprise Data + AI Engineering, Data and AI product teams Business teams that consume enterprise data, technology and AI products, including Marketing, Advertising, Analytics and other enterprise functions Executive leadership and business partners evaluating new capabilities and opportunities External technology partners and vendors, where appropriate